Model Building--Marginal Regression with Dependent Data

模型构建--相关数据的边际回归

• 批准号: 6620909
• 负责人: Wei Pan
• 金额: $ 10.72万
• 依托单位: UNIVERSITY OF MINNESOTA
• 依托单位国家: 美国
• 财政年份: 2002
• 资助国家: 美国
• 起止时间: 2002-01-01 至 2004-12-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/6620909
• 关键词: computer data analysis; computer graphics /printing; computer program /software; computer simulation; computer system design /evaluation; human data; mathematical model; model design /development; statistics /biometry

Dependent response data are common in biomedical studies. One typical example is longitudinal data. Subsequent to the seminal work by Liang and Zeger (1986), marginal regression and its associated generalized estimating equations (GEE) method have become increasingly important in analyzing such data. However, model building, including model checking and model selection, have been relatively neglected for GEE, although there is a large literature in model building for independent data. Since any scientific conclusions drawn from statistical analysis crucially depend on the statistical model being used, and there is always some uncertainty with regard to the correct model due to limited prior knowledge, the importance and necessity of model building are apparent. The subject of this proposed research is model building techniques in marginal regression for dependent data. Specifically, first, formal goodness-of-fit tests are to be investigated. Second, I propose graphical model checking using marginal model plots and the generalized additive model plots. Third, I investigate how to adjust statistical inference with small samples since the commonly used large sample results may not be applicable. The above model building techniques will be evaluated by simulation and using real data. All the techniques will be implemented in the commonly used statistical language S-Plus and made freely available to practitioners.

依赖的响应数据在生物医学研究中很常见。 一个典型的例子是纵向数据。 在Liang and Zeger（1986）的开创性工作之后，边际回归及其相关的广义估计方程（GEE）方法在分析此类数据方面变得越来越重要。 然而，尽管在模型构建中，用于独立数据的模型有很大的文献，但模型构建（包括模型检查和模型选择）已被相对忽略。 由于从统计分析得出的任何科学结论至关重要地取决于所使用的统计模型，并且由于有限的先验知识而导致的正确模型始终存在一些不确定性，因此显而易见的是，模型构建的重要性和必要性是显而易见的。 这项拟议的研究的主题是依赖数据的边际回归中的模型建立技术。 具体而言，首先要研究正式的合适性测试。 其次，我建议使用边际模型图和广义加法模型图进行图形模型检查。 第三，我研究了如何使用小样本调整统计推断，因为常用的大样本结果可能不适用。 上述模型构建技术将通过仿真和使用真实数据来评估。 所有技术都将在常用的统计语言S-Plus中实施，并可以自由地为从业者使用。